Although systematic biases in any part of the ML pipeline can negatively impact model performance gaps, we focus on biases in clinical data caused by disparities in diagnostic/laboratory test orders.
Specifically, we identify an understudied type of label bias caused by disparities in the delivery of or access to clinical care. Previous work on label bias in ML for healthcare studied label misspecification: [1]}, [2]} find that using outcomes such as healthcare costs or certain risk scales as proxies for patient need may disproportionately harm Black patients.
They suggest that training models on redefined outcomes that align better with patient needs may mitigate the negative impacts.
